AI Autonomous Governance Factory: Concept, Architecture, and Launch Pathway

Abstract

Manufacturing is undergoing a paradigm shift from automation to autonomization. Existing concepts such as “lights-out factories” and “unmanned factories” have yet to systematically address the fundamental question: where does a factory’s autonomous capability originate, and who governs it? This paper proposes the concept of the AI Autonomous Governance Factory (AI-AGF), defining its core as an AI system that autonomously completes the full cycle of perception, decision-making, and execution without human intervention, while systematically governing its own objectives, rules, boundaries, and risks. The paper addresses three core propositions: the inevitability argument, the dual-track architecture design, and the four-stage launch pathway. The dual-track architecture consists of the Universal Production Line (UPL) and the Standard Production Line (SPL): the UPL undertakes equipment self-supply and product prototyping, while the SPL handles high-volume production. Both tracks are coordinated by an AI governance layer, forming a physical closed loop of “equipment producing equipment.” The four-dimensional governance framework imposes systematic constraints on AI decision-making across four dimensions—objectives, rules, boundaries, and risks—advancing manufacturing governance from asset-centric to governance-centric, and engaging in structural dialogue with three industrial standards: IEC 62264, RAMI 4.0, and ISO 55000. Unlike existing research that prioritizes the technical dimension, this paper’s distinctive contribution lies in elevating “governance” to an analytical dimension of equal importance to “autonomy”: an AI system must not only make autonomous decisions but also exercise systematic self-constraint and risk control over its own decision logic. Based on the above analysis, this paper presents four operational criteria for AI-AGF (C1–C4), a dual-track architecture blueprint, a four-dimensional governance framework, and a four-stage quantified progressive roadmap. This paper is a prospective review, with its argumentative force deriving from logical coherence, literature support, and systematic engagement with competing hypotheses.